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BackgroundBone marrow aspiration and biopsy remain the gold standard for the diagnosis of hematological diseases despite the development of flow cytometry (FCM) and molecular and gene analyses. However, the interpretation of the results is laborious and operator dependent. Furthermore, the obtained results exhibit inter- and intravariations among specialists. Therefore, it is important to develop a more objective and automated analysis system. Several deep learning models have been developed and applied in medical image analysis but not in the field of hematological histology, especially for bone marrow smear applications. ObjectiveThe aim of this study was to develop a deep learning model (BMSNet) for assisting hematologists in the interpretation of bone marrow smears for faster diagnosis and disease monitoring. MethodsFrom January 1, 2016, to December 31, 2018, 122 bone marrow smears were photographed and divided into a development cohort (N=42), a validation cohort (N=70), and a competition cohort (N=10). The development cohort included 17,319 annotated cells from 291 high-resolution photos. In total, 20 photos were taken for each patient in the validation cohort and the competition cohort. This study included eight annotation categories: erythroid, blasts, myeloid, lymphoid, plasma cells, monocyte, megakaryocyte, and unable to identify. BMSNet is a convolutional neural network with the YOLO v3 architecture, which detects and classifies single cells in a single model. Six visiting staff members participated in a human-machine competition, and the results from the FCM were regarded as the ground truth. ResultsIn the development cohort, according to 6-fold cross-validation, the average precision of the bounding box prediction without consideration of the classification is 67.4%. After removing the bounding box prediction error, the precision and recall of BMSNet were similar to those of the hematologists in most categories. In detecting more than 5% of blasts in the validation cohort, the area under the curve (AUC) of BMSNet (0.948) was higher than the AUC of the hematologists (0.929) but lower than the AUC of the pathologists (0.985). In detecting more than 20% of blasts, the AUCs of the hematologists (0.981) and pathologists (0.980) were similar and were higher than the AUC of BMSNet (0.942). Further analysis showed that the performance difference could be attributed to the myelodysplastic syndrome cases. In the competition cohort, the mean value of the correlations between BMSNet and FCM was 0.960, and the mean values of the correlations between the visiting staff and FCM ranged between 0.952 and 0.990. ConclusionsOur deep learning model can assist hematologists in interpreting bone marrow smears by facilitating and accelerating the detection of hematopoietic cells. However, a detailed morphological interpretation still requires trained hematologists.

While high-dose therapy and autologous stem cell transplant (ASCT) remain integral to the primary treatment of newly diagnosed transplant-elble multiple myeloma (MM) patients, the challenge of disease progression persists. The primary objective of this meta-analysis is to evaluate the efficacy and safety of tandem ASCT compared to single ASCT. We conducted a systematic review and meta-analysis of randomized controlled trials and observational studies comparing tandem ASCT with single ASCT in patients with newly diagnosed MM. We searched PubMed, EMBASE, Cochrane Library, and Clinical Trials databases for studies published up to January 2024. The primary outcomes were progression-free survival (PFS), overall survival (OS), overall response rate (ORR), complete response rate (CRR), and treatment-related mortality (TRM). We used a random-effects model to calculate pooled hazard ratios (HRs) and relative risks (RRs) with 95% confidence intervals (CIs). Study quality was assessed using the Cochrane risk of bias tool and Newcastle–Ottawa Scale. Twelve studies involving 5057 patients met the inclusion criteria. Tandem ASCT was associated with a significantly higher CRR compared to single ASCT (HR 1.33, 95% CI 1.03–1.71, I2 = 15%), but no significant differences were observed in PFS (HR 0.75, 95% CI 0.42–1.34, I2 = 14%), OS (HR 0.60, 95% CI 0.33–1.10, I2 = 27%), or the ORR (RR 0.80, 95% CI 0.59–1.08, I2 = 33%). However, tandem ASCT was associated with a significantly higher risk of TRM (RR 1.78, 95% CI 1.00–3.18, I2 = 0%). Tandem ASCT improves the CRR but does not provide significant benefits in terms of PFS, OS, or ORR compared to single ASCT in patients with newly diagnosed MM. Moreover, tandem ASCT is associated with a higher risk of TRM. The decision to pursue tandem ASCT should be made on an individual basis, carefully weighing the potential benefits and risks in light of each patient's unique clinical situation. Future research should focus on identifying patient subgroups most likely to benefit from tandem ASCT and exploring strategies to optimize the efficacy and safety of this approach in the context of novel agent-based therapies. [ABSTRACT FROM AUTHOR]

Background: Spasticity is a disorder characterized by velocity dependently increasing in the tonic stretch reflexes (muscle tone). There were variable managements for spasticity. Treatment of spasticity depends on the severity, involved part, and patient's and families' preference. However, there were more trials completed in studying the efficacy of extracorporeal shockwave therapy (ESWT) in treating spasticity in different disease. Aim: The goal of our study is to evaluate the efficacy of ESWT in treating of spasticity after central nervous system lesions and to analyze the influences of related factors; we performed a systemic review to survey the effect. Methods: We performed a thoroughly systematic review and meta-analysis. Results: Totally 9 studies were included 4 studies examined the spasticity in stroke group, 4 studies were in cerebral palsy, and 1 study in multiple sclerosis. Regarding the effect of spasticity reduction in overall populations, the pooled effect showed that the modified Ashworth scale grade reduction compared with the baseline values were standardized mean difference (SMD): −4.07 (95% confidence interval (CI), −5.37– −2.76; P < 0.001) immediately after ESWT, SMD: −2.51 (95% CI, −3.40– −1.62; P < 0.001) after 4 weeks, and SMD: −1.44 (95% CI, −1.92– −0.95; P < 0.001) after 12 weeks. In terms of the disease types, the SMD in stroke patients was SMD: −4.03 (95% CI, −5.44– −2.61; Z = 5.57; P < 0.001) immediately after ESWT, SMD: −2.34 (95% CI, −3.01– −1.66; Z = 6.80; P < 0.001) after 4 weeks and SMD: −1.50 (95% CI, −2.06– −0.93; Z = 5.20; P < 0.001) after 12 weeks. No significant adverse events were found. Conclusion: The present meta-analysis revealed that ESWT effectively alleviates spasticity in patients after upper motor neuron lesions, regardless of disease type and parts treated. Both radial and focus ESWTs could decrease spasticity, regardless of the treatment session. The result could last for 12 weeks after treatment. Moreover, no serious side effects were observed after ESWT. Further studies with randomization and more parameters of ESWT were advised to setup to improve the clinical effectiveness. [ABSTRACT FROM AUTHOR]

Background and Objectives: Postoperative adjuvant therapy with uracil and tegafur (UFT) is often used for stage II colon cancer in Japan, but a limited number of studies have investigated the effects of UFT in these patients. Materials and Methods: We conducted a population-based cohort study in patients with resected stage II colon cancer comparing the outcomes after postoperative adjuvant chemotherapy with UFT with an observation-only group. The data were collected from the Taiwan National Health Insurance Research Database from 2000 to 2015. The outcomes of the study were disease-free survival (DFS) and overall survival (OS). The hazard ratios (HRs) were calculated using multivariate Cox proportional hazard regression models. Results: No differences in the DFS and OS were detected between the UFT (1137 patients) and observation (2779 patients) cohorts (DFS: adjusted HR 0.702; 95% confidence interval (CI) 0.489–1.024; p = 0.074) (OS: adjusted HR 0.894; 95% CI 0.542–1.186; p = 0.477). In the subgroup analyses of the different substages, UFT prolonged DFS in patients with stage IIA colon cancer (adjusted HR 0.652; 95% CI 0.352–0.951; p = 0.001) compared with DFS in the observation cohort, but no differences in the OS were detected (adjusted HR 0.734; 95% CI 0.475–1.093; p = 0.503). Conclusions: Our results show that DFS improved significantly in patients with stage IIA colon cancer receiving UFT as a postoperative adjuvant chemotherapy compared with DFS in the observation group. [ABSTRACT FROM AUTHOR]